{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial of MST-DBSCAN in pysda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pysda\n",
    "import os\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import patches as mpatches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Use the build-in functions to read data (highly recommend)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder = r\"D:\\pySDA\\test_data\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 1: Load .csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"DengueKS2014.csv\"\n",
    "path = os.path.join(folder, filename)\n",
    "crs=\"+init=epsg:4326\"\n",
    "\n",
    "pysda_data = pysda.data.readCSV(path, xtitle=\"X\", ytitle=\"Y\", ttitle=\"OnsetDay\", crs=crs, tunit=\"week\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 2: Load DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"DengueKS2014.csv\"\n",
    "path = os.path.join(folder, filename)\n",
    "crs=\"+init=epsg:4326\"\n",
    "df = pd.read_csv(path, encoding=\"utf-8\")\n",
    "\n",
    "pysda_data = pysda.data.readDF(df, xtitle=\"X\", ytitle=\"Y\", ttitle=\"OnsetDay\", crs=crs, tunit=\"week\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 3: Load .shp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"DengueKS2014.shp\"\n",
    "path = os.path.join(folder, filename)\n",
    "\n",
    "pysda_data = pysda.data.readSHP(path, ttitle=\"OnsetDay\", tunit=\"week\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 4: Load geoDataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"DengueKS2014.shp\"\n",
    "path = os.path.join(folder, filename)\n",
    "gdf = gpd.read_file(path, encoding=\"utf-8\")\n",
    "\n",
    "pysda_data = pysda.data.readGDF(gdf, ttitle=\"OnsetDay\", tunit=\"week\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Arguments explanation:\n",
    "1. **xtitle** is the name of the column which records the x coordinate of each point.\n",
    "2. **ytitle** is the name of the column which records the y coordinate of each point.  \n",
    "Note: The values of x and y must be **projected** coordinates rather than longitude and latitude.\n",
    "3. **crs** is the Coordinate Reference System of the x and y.\n",
    "\n",
    "4. **ttitle** is the name of the column which records the time of each point either in integer format or in date format. If its values are in integer format, pysda will directly use them as the time stamps; otherwise, pysda will firstly transform them into integer format (through *tunit* argument).\n",
    "\n",
    "5. **tunit** is the temporal resolution for analysis, and the first time stamp is the first date in the input data. There are several choices:\n",
    "    - int: assign that the ttitle column in the input data is integer format.\n",
    "    - hour: 1 hour  \n",
    "    - day: 1 days\n",
    "    - week: 7 days\n",
    "    - month: 30 days\n",
    "    - year: 365 days\n",
    "    - tunit also can be any string accepted by pandas' *date_range* function. For more details, please refer to https://pandas.pydata.org/pandas-docs/stable/timeseries.html#timeseries-offset-aliases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "***\n",
    "### Initialize an instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mst = pysda.MSTDBSCAN(pysda_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set parameters \n",
    "#### Basic parameters:\n",
    "- eps_spatial: spatial search radius \n",
    "- eps_temporalLow: the minimum value of the temporal search window \n",
    "- eps_temporalHigh: the maximum value of the temporal search window\n",
    "- mis_pts: The minimum number of neughbors for a point to become a *core* and further form a cluster\n",
    "\n",
    "#### Advanced parameters:\n",
    "- movingRatio (default is 0.1): \n",
    "    This is the threshold value to check whether a cluster's center moves.\n",
    "    When the distance between centers in two sequential times over the eps_spatial is greater than the value, a cluster is considered as moving its center; otherwise, it stays.\n",
    "    \n",
    "- areaRatio (default is 0.1):\n",
    "    This is the threshold value to check whether a cluster's area changes. \n",
    "    When the **absolute** difference of area in two sequential times is greater than the areaRatio, a cluster is considered as changing its area; otherwise, it keeps a similar area.  \n",
    "  \n",
    "#### Further details: \n",
    "For more details about the meanging of each parameter, please refer to the following documents:\n",
    "- journal article: https://www.tandfonline.com/doi/full/10.1080/24694452.2017.1407630"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "eps_spatial = 300\n",
    "eps_temporalLow = 1\n",
    "eps_temporalHigh = 2\n",
    "min_pts = 3\n",
    "movingRatio = 0.1\n",
    "areaRatio = 0.1\n",
    "\n",
    "mst.setParams(eps_spatial, eps_temporalLow, eps_temporalHigh, min_pts, movingRatio, areaRatio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Start clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "core algorithm costs --- 1.9986937046051025 seconds ---\n"
     ]
    }
   ],
   "source": [
    "mst.run()\n",
    "result = mst.result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "***\n",
    "### Get the result of dynamic clusters\n",
    "Explaination about the columns:\n",
    "- **clusterID**: the ID of a cluster.  \n",
    "    (Note: the value starts from 0, so 0 is the first cluster.)\n",
    "        \n",
    "- **mstTime**: a specific time (integer format) when a point is still alive.\n",
    "- **mstDate**: a specific time (date format) when a point is still alive. \n",
    "    \n",
    "- **type**: the evolution type of a cluster at a specific time.  \n",
    "    (Note: There are 10 possible types. Please refer to the paper for a detailed description.)\n",
    "        \n",
    "- **centerX**, **centerY**: the coordinate of the center of a cluster at a specific time.\n",
    "    \n",
    "- **shape**: the polygon of a cluster at a specific time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clusterID</th>\n",
       "      <th>mstTime</th>\n",
       "      <th>type</th>\n",
       "      <th>centerX</th>\n",
       "      <th>centerY</th>\n",
       "      <th>shape</th>\n",
       "      <th>mstDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Emerge</td>\n",
       "      <td>179695.200767</td>\n",
       "      <td>2.497270e+06</td>\n",
       "      <td>POLYGON ((179995.200766882 2497269.858528782, ...</td>\n",
       "      <td>2014/05/20-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Steady</td>\n",
       "      <td>179695.200767</td>\n",
       "      <td>2.497270e+06</td>\n",
       "      <td>POLYGON ((179995.200766882 2497269.858528782, ...</td>\n",
       "      <td>2014/05/27-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Emerge</td>\n",
       "      <td>179695.200767</td>\n",
       "      <td>2.497270e+06</td>\n",
       "      <td>POLYGON ((179995.200766882 2497269.858528782, ...</td>\n",
       "      <td>2014/05/20-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Steady</td>\n",
       "      <td>179695.200767</td>\n",
       "      <td>2.497270e+06</td>\n",
       "      <td>POLYGON ((179995.200766882 2497269.858528782, ...</td>\n",
       "      <td>2014/05/27-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Emerge</td>\n",
       "      <td>181485.930365</td>\n",
       "      <td>2.496833e+06</td>\n",
       "      <td>POLYGON ((181720.6486981525 2496999.610708646,...</td>\n",
       "      <td>2014/06/03-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Split</td>\n",
       "      <td>180034.940433</td>\n",
       "      <td>2.497915e+06</td>\n",
       "      <td>POLYGON ((180300.45924644 2497908.511059511, 1...</td>\n",
       "      <td>2014/06/17-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Split</td>\n",
       "      <td>180034.940433</td>\n",
       "      <td>2.497915e+06</td>\n",
       "      <td>POLYGON ((180300.45924644 2497908.511059511, 1...</td>\n",
       "      <td>2014/06/17-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>Split</td>\n",
       "      <td>180034.940433</td>\n",
       "      <td>2.497915e+06</td>\n",
       "      <td>POLYGON ((180300.45924644 2497908.511059511, 1...</td>\n",
       "      <td>2014/06/17-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>Split</td>\n",
       "      <td>180034.940433</td>\n",
       "      <td>2.497915e+06</td>\n",
       "      <td>POLYGON ((180300.45924644 2497908.511059511, 1...</td>\n",
       "      <td>2014/06/17-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>Split</td>\n",
       "      <td>180034.940433</td>\n",
       "      <td>2.497915e+06</td>\n",
       "      <td>POLYGON ((180300.45924644 2497908.511059511, 1...</td>\n",
       "      <td>2014/06/17-00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clusterID  mstTime    type        centerX       centerY  \\\n",
       "0          0        1  Emerge  179695.200767  2.497270e+06   \n",
       "1          0        2  Steady  179695.200767  2.497270e+06   \n",
       "2          1        1  Emerge  179695.200767  2.497270e+06   \n",
       "3          1        2  Steady  179695.200767  2.497270e+06   \n",
       "4          3        3  Emerge  181485.930365  2.496833e+06   \n",
       "5          4        5   Split  180034.940433  2.497915e+06   \n",
       "6          5        5   Split  180034.940433  2.497915e+06   \n",
       "7          6        5   Split  180034.940433  2.497915e+06   \n",
       "8          7        5   Split  180034.940433  2.497915e+06   \n",
       "9          8        5   Split  180034.940433  2.497915e+06   \n",
       "\n",
       "                                               shape              mstDate  \n",
       "0  POLYGON ((179995.200766882 2497269.858528782, ...  2014/05/20-00:00:00  \n",
       "1  POLYGON ((179995.200766882 2497269.858528782, ...  2014/05/27-00:00:00  \n",
       "2  POLYGON ((179995.200766882 2497269.858528782, ...  2014/05/20-00:00:00  \n",
       "3  POLYGON ((179995.200766882 2497269.858528782, ...  2014/05/27-00:00:00  \n",
       "4  POLYGON ((181720.6486981525 2496999.610708646,...  2014/06/03-00:00:00  \n",
       "5  POLYGON ((180300.45924644 2497908.511059511, 1...  2014/06/17-00:00:00  \n",
       "6  POLYGON ((180300.45924644 2497908.511059511, 1...  2014/06/17-00:00:00  \n",
       "7  POLYGON ((180300.45924644 2497908.511059511, 1...  2014/06/17-00:00:00  \n",
       "8  POLYGON ((180300.45924644 2497908.511059511, 1...  2014/06/17-00:00:00  \n",
       "9  POLYGON ((180300.45924644 2497908.511059511, 1...  2014/06/17-00:00:00  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusterGDF = result.clusters\n",
    "clusterGDF.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load a polygon .shp for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"KSVillage.shp\"\n",
    "path = os.path.join(folder, filename)\n",
    "polygonGDF = gpd.read_file(path, encoding=\"utf-8\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23fa7fd13c8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1152 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "part = clusterGDF[clusterGDF[\"mstDate\"] == '2014/08/26-00:00:00']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,16))\n",
    "ax.set_aspect(\"equal\")\n",
    "ax.set_title(\"clusters in the week starting from 2014/08/26\")\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "\n",
    "polygonGDF.plot(ax=ax, color=\"gray\")\n",
    "part.plot(ax=ax, categorical=True, column=\"clusterID\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "***\n",
    "### Get the result of dynamic points\n",
    "Explaination about the columns:\n",
    "- **pointID**: the original ID of a point.\n",
    "    \n",
    "- **mstTime**: a specific time (integer format) when a point is still alive.\n",
    "\n",
    "- **mstDate**: a specific time (date format) when a point is still alive.\n",
    "    \n",
    "- **clusterID**: the cluster to which a point belongs at a specific time.  \n",
    "  (Note: -1 means the point does not belong to any cluster, so its role must be noise.)\n",
    "        \n",
    "- **role**: the role (core, border or noise) of a point at a specific time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pointID</th>\n",
       "      <th>mstTime</th>\n",
       "      <th>role</th>\n",
       "      <th>clusterID</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index</th>\n",
       "      <th>OnsetDay</th>\n",
       "      <th>GuessDay</th>\n",
       "      <th>InformDay</th>\n",
       "      <th>Gender</th>\n",
       "      <th>...</th>\n",
       "      <th>Number</th>\n",
       "      <th>LiveVill_1</th>\n",
       "      <th>InfectVi_1</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Week</th>\n",
       "      <th>FDW</th>\n",
       "      <th>startTime</th>\n",
       "      <th>intTime</th>\n",
       "      <th>mstDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>noise</td>\n",
       "      <td>-1</td>\n",
       "      <td>POINT (179695.200766882 2497269.858528782)</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/13</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>男</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2014/05/12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/13-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>core</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (179695.200766882 2497269.858528782)</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/13</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>男</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2014/05/12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/20-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1251</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>border</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (179695.200766882 2497269.858528782)</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/13</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>男</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2014/05/12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/20-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>core</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (179695.200766882 2497269.858528782)</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/13</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>男</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2014/05/12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/27-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>border</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (179695.200766882 2497269.858528782)</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/13</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>男</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2014/05/12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/27-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>noise</td>\n",
       "      <td>-1</td>\n",
       "      <td>POINT (179695.200766882 2497269.858528782)</td>\n",
       "      <td>1</td>\n",
       "      <td>2014/05/13</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>男</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2014/05/12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/13-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>core</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (179695.200766882 2497269.858528782)</td>\n",
       "      <td>1</td>\n",
       "      <td>2014/05/13</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>男</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2014/05/12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/20-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>border</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (179695.200766882 2497269.858528782)</td>\n",
       "      <td>1</td>\n",
       "      <td>2014/05/13</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>男</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2014/05/12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/20-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>core</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (179695.200766882 2497269.858528782)</td>\n",
       "      <td>1</td>\n",
       "      <td>2014/05/13</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>男</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2014/05/12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/27-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>border</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (179695.200766882 2497269.858528782)</td>\n",
       "      <td>1</td>\n",
       "      <td>2014/05/13</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>男</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2014/05/12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/27-00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      pointID  mstTime    role  clusterID  \\\n",
       "0           0        0   noise         -1   \n",
       "1250        0        1    core          0   \n",
       "1251        0        1  border          0   \n",
       "1252        0        2    core          0   \n",
       "1253        0        2  border          0   \n",
       "1           1        0   noise         -1   \n",
       "1254        1        1    core          1   \n",
       "1255        1        1  border          1   \n",
       "1256        1        2    core          1   \n",
       "1257        1        2  border          1   \n",
       "\n",
       "                                        geometry  index    OnsetDay  \\\n",
       "0     POINT (179695.200766882 2497269.858528782)      0  2014/05/13   \n",
       "1250  POINT (179695.200766882 2497269.858528782)      0  2014/05/13   \n",
       "1251  POINT (179695.200766882 2497269.858528782)      0  2014/05/13   \n",
       "1252  POINT (179695.200766882 2497269.858528782)      0  2014/05/13   \n",
       "1253  POINT (179695.200766882 2497269.858528782)      0  2014/05/13   \n",
       "1     POINT (179695.200766882 2497269.858528782)      1  2014/05/13   \n",
       "1254  POINT (179695.200766882 2497269.858528782)      1  2014/05/13   \n",
       "1255  POINT (179695.200766882 2497269.858528782)      1  2014/05/13   \n",
       "1256  POINT (179695.200766882 2497269.858528782)      1  2014/05/13   \n",
       "1257  POINT (179695.200766882 2497269.858528782)      1  2014/05/13   \n",
       "\n",
       "        GuessDay   InformDay Gender         ...          Number   LiveVill_1  \\\n",
       "0     2014/06/06  2014/06/06      男         ...               1  6400900-002   \n",
       "1250  2014/06/06  2014/06/06      男         ...               1  6400900-002   \n",
       "1251  2014/06/06  2014/06/06      男         ...               1  6400900-002   \n",
       "1252  2014/06/06  2014/06/06      男         ...               1  6400900-002   \n",
       "1253  2014/06/06  2014/06/06      男         ...               1  6400900-002   \n",
       "1     2014/06/06  2014/06/06      男         ...               1  6400900-002   \n",
       "1254  2014/06/06  2014/06/06      男         ...               1  6400900-002   \n",
       "1255  2014/06/06  2014/06/06      男         ...               1  6400900-002   \n",
       "1256  2014/06/06  2014/06/06      男         ...               1  6400900-002   \n",
       "1257  2014/06/06  2014/06/06      男         ...               1  6400900-002   \n",
       "\n",
       "       InfectVi_1  Year Month  Week         FDW startTime intTime  \\\n",
       "0     6400900-002  2014     5    20  2014/05/12         1       0   \n",
       "1250  6400900-002  2014     5    20  2014/05/12         1       0   \n",
       "1251  6400900-002  2014     5    20  2014/05/12         1       0   \n",
       "1252  6400900-002  2014     5    20  2014/05/12         1       0   \n",
       "1253  6400900-002  2014     5    20  2014/05/12         1       0   \n",
       "1     6400900-002  2014     5    20  2014/05/12         1       0   \n",
       "1254  6400900-002  2014     5    20  2014/05/12         1       0   \n",
       "1255  6400900-002  2014     5    20  2014/05/12         1       0   \n",
       "1256  6400900-002  2014     5    20  2014/05/12         1       0   \n",
       "1257  6400900-002  2014     5    20  2014/05/12         1       0   \n",
       "\n",
       "                  mstDate  \n",
       "0     2014/05/13-00:00:00  \n",
       "1250  2014/05/20-00:00:00  \n",
       "1251  2014/05/20-00:00:00  \n",
       "1252  2014/05/27-00:00:00  \n",
       "1253  2014/05/27-00:00:00  \n",
       "1     2014/05/13-00:00:00  \n",
       "1254  2014/05/20-00:00:00  \n",
       "1255  2014/05/20-00:00:00  \n",
       "1256  2014/05/27-00:00:00  \n",
       "1257  2014/05/27-00:00:00  \n",
       "\n",
       "[10 rows x 34 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pointGDF = result.points\n",
    "pointGDF.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "***\n",
    "### Add polygon data to get the result of further analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "result.setPolygons(polygonGDF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the result of polygons\n",
    "Explaination about the columns:  \n",
    "- **DZ**: diffusion zones. The polygons that are assigned to the same zone undergo a similar diffusion procedure.\n",
    "\n",
    "- **The dates**: each date is a mstDate. Each column records the situations (increas, decrease, keep, or no cluster) that every polygons undergo at that time.\n",
    "\n",
    "    - increase: a polygon is covered by a cluster whose evolution type is Emerge, Growth, Directional growth, or Merge. \n",
    "    - decrease: a polygon is covered by a cluster whose evolution type is Reduction, Directional reduction, or Split.\n",
    "    - keep: a polygon is covered by a cluster whose evolution type is Steady, Move, or SplitMerge.\n",
    "    - no cluster: a polygon is not covered by any cluster.\n",
    "            \n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COUNTY_ID</th>\n",
       "      <th>VILLAGE_ID</th>\n",
       "      <th>VILLAGE</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>TOWN</th>\n",
       "      <th>V_ID</th>\n",
       "      <th>TOWN_ID</th>\n",
       "      <th>H_CNT</th>\n",
       "      <th>P_CNT</th>\n",
       "      <th>M_CNT</th>\n",
       "      <th>...</th>\n",
       "      <th>2014/06/24-00:00:00</th>\n",
       "      <th>2014/07/01-00:00:00</th>\n",
       "      <th>2014/07/08-00:00:00</th>\n",
       "      <th>2014/07/15-00:00:00</th>\n",
       "      <th>2014/07/22-00:00:00</th>\n",
       "      <th>2014/07/29-00:00:00</th>\n",
       "      <th>2014/08/05-00:00:00</th>\n",
       "      <th>2014/08/12-00:00:00</th>\n",
       "      <th>2014/08/19-00:00:00</th>\n",
       "      <th>2014/08/26-00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64000</td>\n",
       "      <td>066</td>\n",
       "      <td>正仁里</td>\n",
       "      <td>高雄市</td>\n",
       "      <td>苓雅區</td>\n",
       "      <td>64000080-066</td>\n",
       "      <td>64000080</td>\n",
       "      <td>1174.0</td>\n",
       "      <td>3047.0</td>\n",
       "      <td>1464.0</td>\n",
       "      <td>...</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>increase</td>\n",
       "      <td>decrease</td>\n",
       "      <td>decrease</td>\n",
       "      <td>no cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64000</td>\n",
       "      <td>067</td>\n",
       "      <td>文昌里</td>\n",
       "      <td>高雄市</td>\n",
       "      <td>苓雅區</td>\n",
       "      <td>64000080-067</td>\n",
       "      <td>64000080</td>\n",
       "      <td>1670.0</td>\n",
       "      <td>4474.0</td>\n",
       "      <td>2171.0</td>\n",
       "      <td>...</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>increase</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64000</td>\n",
       "      <td>068</td>\n",
       "      <td>建軍里</td>\n",
       "      <td>高雄市</td>\n",
       "      <td>苓雅區</td>\n",
       "      <td>64000080-068</td>\n",
       "      <td>64000080</td>\n",
       "      <td>846.0</td>\n",
       "      <td>2361.0</td>\n",
       "      <td>1209.0</td>\n",
       "      <td>...</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>increase</td>\n",
       "      <td>decrease</td>\n",
       "      <td>decrease</td>\n",
       "      <td>no cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64000</td>\n",
       "      <td>069</td>\n",
       "      <td>衛武里</td>\n",
       "      <td>高雄市</td>\n",
       "      <td>苓雅區</td>\n",
       "      <td>64000080-069</td>\n",
       "      <td>64000080</td>\n",
       "      <td>970.0</td>\n",
       "      <td>2323.0</td>\n",
       "      <td>1177.0</td>\n",
       "      <td>...</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>increase</td>\n",
       "      <td>decrease</td>\n",
       "      <td>decrease</td>\n",
       "      <td>no cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64000</td>\n",
       "      <td>001</td>\n",
       "      <td>草衙里</td>\n",
       "      <td>高雄市</td>\n",
       "      <td>前鎮區</td>\n",
       "      <td>64000090-001</td>\n",
       "      <td>64000090</td>\n",
       "      <td>4206.0</td>\n",
       "      <td>11042.0</td>\n",
       "      <td>5455.0</td>\n",
       "      <td>...</td>\n",
       "      <td>decrease</td>\n",
       "      <td>keep</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>increase</td>\n",
       "      <td>increase</td>\n",
       "      <td>decrease</td>\n",
       "      <td>increase</td>\n",
       "      <td>decrease</td>\n",
       "      <td>keep</td>\n",
       "      <td>no cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>64000</td>\n",
       "      <td>002</td>\n",
       "      <td>明孝里</td>\n",
       "      <td>高雄市</td>\n",
       "      <td>前鎮區</td>\n",
       "      <td>64000090-002</td>\n",
       "      <td>64000090</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>2807.0</td>\n",
       "      <td>1423.0</td>\n",
       "      <td>...</td>\n",
       "      <td>decrease</td>\n",
       "      <td>keep</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>64000</td>\n",
       "      <td>003</td>\n",
       "      <td>明正里</td>\n",
       "      <td>高雄市</td>\n",
       "      <td>前鎮區</td>\n",
       "      <td>64000090-003</td>\n",
       "      <td>64000090</td>\n",
       "      <td>2558.0</td>\n",
       "      <td>5959.0</td>\n",
       "      <td>3059.0</td>\n",
       "      <td>...</td>\n",
       "      <td>decrease</td>\n",
       "      <td>keep</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>increase</td>\n",
       "      <td>increase</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>increase</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>increase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>64000</td>\n",
       "      <td>004</td>\n",
       "      <td>明義里</td>\n",
       "      <td>高雄市</td>\n",
       "      <td>前鎮區</td>\n",
       "      <td>64000090-004</td>\n",
       "      <td>64000090</td>\n",
       "      <td>1117.0</td>\n",
       "      <td>2694.0</td>\n",
       "      <td>1361.0</td>\n",
       "      <td>...</td>\n",
       "      <td>decrease</td>\n",
       "      <td>keep</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>increase</td>\n",
       "      <td>increase</td>\n",
       "      <td>decrease</td>\n",
       "      <td>increase</td>\n",
       "      <td>decrease</td>\n",
       "      <td>keep</td>\n",
       "      <td>no cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>64000</td>\n",
       "      <td>005</td>\n",
       "      <td>仁愛里</td>\n",
       "      <td>高雄市</td>\n",
       "      <td>前鎮區</td>\n",
       "      <td>64000090-005</td>\n",
       "      <td>64000090</td>\n",
       "      <td>715.0</td>\n",
       "      <td>1527.0</td>\n",
       "      <td>782.0</td>\n",
       "      <td>...</td>\n",
       "      <td>decrease</td>\n",
       "      <td>keep</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>64000</td>\n",
       "      <td>006</td>\n",
       "      <td>德昌里</td>\n",
       "      <td>高雄市</td>\n",
       "      <td>前鎮區</td>\n",
       "      <td>64000090-006</td>\n",
       "      <td>64000090</td>\n",
       "      <td>975.0</td>\n",
       "      <td>2292.0</td>\n",
       "      <td>1142.0</td>\n",
       "      <td>...</td>\n",
       "      <td>decrease</td>\n",
       "      <td>keep</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  COUNTY_ID VILLAGE_ID VILLAGE COUNTY TOWN          V_ID   TOWN_ID   H_CNT  \\\n",
       "0     64000        066     正仁里    高雄市  苓雅區  64000080-066  64000080  1174.0   \n",
       "1     64000        067     文昌里    高雄市  苓雅區  64000080-067  64000080  1670.0   \n",
       "2     64000        068     建軍里    高雄市  苓雅區  64000080-068  64000080   846.0   \n",
       "3     64000        069     衛武里    高雄市  苓雅區  64000080-069  64000080   970.0   \n",
       "4     64000        001     草衙里    高雄市  前鎮區  64000090-001  64000090  4206.0   \n",
       "5     64000        002     明孝里    高雄市  前鎮區  64000090-002  64000090  1150.0   \n",
       "6     64000        003     明正里    高雄市  前鎮區  64000090-003  64000090  2558.0   \n",
       "7     64000        004     明義里    高雄市  前鎮區  64000090-004  64000090  1117.0   \n",
       "8     64000        005     仁愛里    高雄市  前鎮區  64000090-005  64000090   715.0   \n",
       "9     64000        006     德昌里    高雄市  前鎮區  64000090-006  64000090   975.0   \n",
       "\n",
       "     P_CNT   M_CNT         ...          2014/06/24-00:00:00  \\\n",
       "0   3047.0  1464.0         ...                   no cluster   \n",
       "1   4474.0  2171.0         ...                   no cluster   \n",
       "2   2361.0  1209.0         ...                   no cluster   \n",
       "3   2323.0  1177.0         ...                   no cluster   \n",
       "4  11042.0  5455.0         ...                     decrease   \n",
       "5   2807.0  1423.0         ...                     decrease   \n",
       "6   5959.0  3059.0         ...                     decrease   \n",
       "7   2694.0  1361.0         ...                     decrease   \n",
       "8   1527.0   782.0         ...                     decrease   \n",
       "9   2292.0  1142.0         ...                     decrease   \n",
       "\n",
       "  2014/07/01-00:00:00  2014/07/08-00:00:00  2014/07/15-00:00:00  \\\n",
       "0          no cluster           no cluster           no cluster   \n",
       "1          no cluster           no cluster           no cluster   \n",
       "2          no cluster           no cluster           no cluster   \n",
       "3          no cluster           no cluster           no cluster   \n",
       "4                keep           no cluster             increase   \n",
       "5                keep           no cluster           no cluster   \n",
       "6                keep           no cluster             increase   \n",
       "7                keep           no cluster             increase   \n",
       "8                keep           no cluster           no cluster   \n",
       "9                keep           no cluster           no cluster   \n",
       "\n",
       "   2014/07/22-00:00:00 2014/07/29-00:00:00  2014/08/05-00:00:00  \\\n",
       "0           no cluster          no cluster             increase   \n",
       "1           no cluster          no cluster             increase   \n",
       "2           no cluster          no cluster             increase   \n",
       "3           no cluster          no cluster             increase   \n",
       "4             increase            decrease             increase   \n",
       "5           no cluster          no cluster           no cluster   \n",
       "6             increase          no cluster             increase   \n",
       "7             increase            decrease             increase   \n",
       "8           no cluster          no cluster           no cluster   \n",
       "9           no cluster          no cluster           no cluster   \n",
       "\n",
       "  2014/08/12-00:00:00 2014/08/19-00:00:00 2014/08/26-00:00:00  \n",
       "0            decrease            decrease          no cluster  \n",
       "1          no cluster          no cluster          no cluster  \n",
       "2            decrease            decrease          no cluster  \n",
       "3            decrease            decrease          no cluster  \n",
       "4            decrease                keep          no cluster  \n",
       "5          no cluster          no cluster          no cluster  \n",
       "6          no cluster          no cluster            increase  \n",
       "7            decrease                keep          no cluster  \n",
       "8          no cluster          no cluster          no cluster  \n",
       "9          no cluster          no cluster          no cluster  \n",
       "\n",
       "[10 rows x 33 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polygonResultGDF = result.polygons\n",
    "polygonResultGDF.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ploat diffusion zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,16))\n",
    "ax.set_aspect(\"equal\")\n",
    "ax.set_title(column[0:10])\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### plot polygons' dynamic "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1152 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "column = '2014/08/26-00:00:00'\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,16))\n",
    "ax.set_aspect(\"equal\")\n",
    "ax.set_title(column[0:10])\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "\n",
    "increase_patch = mpatches.Patch(color=\"red\", label='Increase')\n",
    "keep_patch = mpatches.Patch(color=\"blue\", label='Keep')\n",
    "decrease_patch = mpatches.Patch(color=\"green\", label='Decrease')\n",
    "non_patch = mpatches.Patch(color=\"gray\", label='No cluster')\n",
    "\n",
    "ax.legend(handles=[increase_patch, keep_patch, decrease_patch, non_patch])\n",
    "\n",
    "\n",
    "groupedGDF = polygonResultGDF.groupby(column)\n",
    "for name, group in groupedGDF:\n",
    "    if name == \"increase\":\n",
    "        group.plot(ax=ax, color=\"red\")\n",
    "    elif name == \"keep\":\n",
    "        group.plot(ax=ax, color=\"blue\")\n",
    "    elif name == \"decrease\":\n",
    "        group.plot(ax=ax, color=\"green\")\n",
    "    else:\n",
    "        group.plot(ax=ax, color=\"gray\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "***\n",
    "### Get all results (clusters, points and polygons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clusterID</th>\n",
       "      <th>mstTime</th>\n",
       "      <th>type</th>\n",
       "      <th>centerX</th>\n",
       "      <th>centerY</th>\n",
       "      <th>shape</th>\n",
       "      <th>mstDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Emerge</td>\n",
       "      <td>179695.200767</td>\n",
       "      <td>2.497270e+06</td>\n",
       "      <td>POLYGON ((179995.200766882 2497269.858528782, ...</td>\n",
       "      <td>2014/05/20-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Steady</td>\n",
       "      <td>179695.200767</td>\n",
       "      <td>2.497270e+06</td>\n",
       "      <td>POLYGON ((179995.200766882 2497269.858528782, ...</td>\n",
       "      <td>2014/05/27-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Emerge</td>\n",
       "      <td>179695.200767</td>\n",
       "      <td>2.497270e+06</td>\n",
       "      <td>POLYGON ((179995.200766882 2497269.858528782, ...</td>\n",
       "      <td>2014/05/20-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Steady</td>\n",
       "      <td>179695.200767</td>\n",
       "      <td>2.497270e+06</td>\n",
       "      <td>POLYGON ((179995.200766882 2497269.858528782, ...</td>\n",
       "      <td>2014/05/27-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Emerge</td>\n",
       "      <td>181485.930365</td>\n",
       "      <td>2.496833e+06</td>\n",
       "      <td>POLYGON ((181720.6486981525 2496999.610708646,...</td>\n",
       "      <td>2014/06/03-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Split</td>\n",
       "      <td>180034.940433</td>\n",
       "      <td>2.497915e+06</td>\n",
       "      <td>POLYGON ((180300.45924644 2497908.511059511, 1...</td>\n",
       "      <td>2014/06/17-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Split</td>\n",
       "      <td>180034.940433</td>\n",
       "      <td>2.497915e+06</td>\n",
       "      <td>POLYGON ((180300.45924644 2497908.511059511, 1...</td>\n",
       "      <td>2014/06/17-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>Split</td>\n",
       "      <td>180034.940433</td>\n",
       "      <td>2.497915e+06</td>\n",
       "      <td>POLYGON ((180300.45924644 2497908.511059511, 1...</td>\n",
       "      <td>2014/06/17-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>Split</td>\n",
       "      <td>180034.940433</td>\n",
       "      <td>2.497915e+06</td>\n",
       "      <td>POLYGON ((180300.45924644 2497908.511059511, 1...</td>\n",
       "      <td>2014/06/17-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>Split</td>\n",
       "      <td>180034.940433</td>\n",
       "      <td>2.497915e+06</td>\n",
       "      <td>POLYGON ((180300.45924644 2497908.511059511, 1...</td>\n",
       "      <td>2014/06/17-00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clusterID  mstTime    type        centerX       centerY  \\\n",
       "0          0        1  Emerge  179695.200767  2.497270e+06   \n",
       "1          0        2  Steady  179695.200767  2.497270e+06   \n",
       "2          1        1  Emerge  179695.200767  2.497270e+06   \n",
       "3          1        2  Steady  179695.200767  2.497270e+06   \n",
       "4          3        3  Emerge  181485.930365  2.496833e+06   \n",
       "5          4        5   Split  180034.940433  2.497915e+06   \n",
       "6          5        5   Split  180034.940433  2.497915e+06   \n",
       "7          6        5   Split  180034.940433  2.497915e+06   \n",
       "8          7        5   Split  180034.940433  2.497915e+06   \n",
       "9          8        5   Split  180034.940433  2.497915e+06   \n",
       "\n",
       "                                               shape              mstDate  \n",
       "0  POLYGON ((179995.200766882 2497269.858528782, ...  2014/05/20-00:00:00  \n",
       "1  POLYGON ((179995.200766882 2497269.858528782, ...  2014/05/27-00:00:00  \n",
       "2  POLYGON ((179995.200766882 2497269.858528782, ...  2014/05/20-00:00:00  \n",
       "3  POLYGON ((179995.200766882 2497269.858528782, ...  2014/05/27-00:00:00  \n",
       "4  POLYGON ((181720.6486981525 2496999.610708646,...  2014/06/03-00:00:00  \n",
       "5  POLYGON ((180300.45924644 2497908.511059511, 1...  2014/06/17-00:00:00  \n",
       "6  POLYGON ((180300.45924644 2497908.511059511, 1...  2014/06/17-00:00:00  \n",
       "7  POLYGON ((180300.45924644 2497908.511059511, 1...  2014/06/17-00:00:00  \n",
       "8  POLYGON ((180300.45924644 2497908.511059511, 1...  2014/06/17-00:00:00  \n",
       "9  POLYGON ((180300.45924644 2497908.511059511, 1...  2014/06/17-00:00:00  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allResults = result.getAll()\n",
    "clusters = allResults[\"clusters\"]\n",
    "clusters.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pointID</th>\n",
       "      <th>mstTime</th>\n",
       "      <th>role</th>\n",
       "      <th>clusterID</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index</th>\n",
       "      <th>OnsetDay</th>\n",
       "      <th>GuessDay</th>\n",
       "      <th>InformDay</th>\n",
       "      <th>Gender</th>\n",
       "      <th>...</th>\n",
       "      <th>Number</th>\n",
       "      <th>LiveVill_1</th>\n",
       "      <th>InfectVi_1</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Week</th>\n",
       "      <th>FDW</th>\n",
       "      <th>startTime</th>\n",
       "      <th>intTime</th>\n",
       "      <th>mstDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>noise</td>\n",
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       "      <td>POINT (179695.200766882 2497269.858528782)</td>\n",
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       "      <td>2014/06/06</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>男</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2014/05/12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/13-00:00:00</td>\n",
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       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>core</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (179695.200766882 2497269.858528782)</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/13</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>男</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>6400900-002</td>\n",
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       "      <td>2014/05/12</td>\n",
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       "      <td>0</td>\n",
       "      <td>2014/05/20-00:00:00</td>\n",
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       "    <tr>\n",
       "      <th>1251</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>border</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (179695.200766882 2497269.858528782)</td>\n",
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       "      <td>2014/05/13</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>男</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2014/05/12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/20-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>core</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (179695.200766882 2497269.858528782)</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/13</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>男</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2014/05/12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/27-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>border</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (179695.200766882 2497269.858528782)</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/13</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>男</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2014/05/12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/27-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>noise</td>\n",
       "      <td>-1</td>\n",
       "      <td>POINT (179695.200766882 2497269.858528782)</td>\n",
       "      <td>1</td>\n",
       "      <td>2014/05/13</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>男</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2014/05/12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/13-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>core</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (179695.200766882 2497269.858528782)</td>\n",
       "      <td>1</td>\n",
       "      <td>2014/05/13</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>男</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2014/05/12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/20-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>border</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (179695.200766882 2497269.858528782)</td>\n",
       "      <td>1</td>\n",
       "      <td>2014/05/13</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>男</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2014/05/12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/20-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>core</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (179695.200766882 2497269.858528782)</td>\n",
       "      <td>1</td>\n",
       "      <td>2014/05/13</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>男</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2014/05/12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/27-00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>border</td>\n",
       "      <td>1</td>\n",
       "      <td>POINT (179695.200766882 2497269.858528782)</td>\n",
       "      <td>1</td>\n",
       "      <td>2014/05/13</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>2014/06/06</td>\n",
       "      <td>男</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>6400900-002</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2014/05/12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014/05/27-00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      pointID  mstTime    role  clusterID  \\\n",
       "0           0        0   noise         -1   \n",
       "1250        0        1    core          0   \n",
       "1251        0        1  border          0   \n",
       "1252        0        2    core          0   \n",
       "1253        0        2  border          0   \n",
       "1           1        0   noise         -1   \n",
       "1254        1        1    core          1   \n",
       "1255        1        1  border          1   \n",
       "1256        1        2    core          1   \n",
       "1257        1        2  border          1   \n",
       "\n",
       "                                        geometry  index    OnsetDay  \\\n",
       "0     POINT (179695.200766882 2497269.858528782)      0  2014/05/13   \n",
       "1250  POINT (179695.200766882 2497269.858528782)      0  2014/05/13   \n",
       "1251  POINT (179695.200766882 2497269.858528782)      0  2014/05/13   \n",
       "1252  POINT (179695.200766882 2497269.858528782)      0  2014/05/13   \n",
       "1253  POINT (179695.200766882 2497269.858528782)      0  2014/05/13   \n",
       "1     POINT (179695.200766882 2497269.858528782)      1  2014/05/13   \n",
       "1254  POINT (179695.200766882 2497269.858528782)      1  2014/05/13   \n",
       "1255  POINT (179695.200766882 2497269.858528782)      1  2014/05/13   \n",
       "1256  POINT (179695.200766882 2497269.858528782)      1  2014/05/13   \n",
       "1257  POINT (179695.200766882 2497269.858528782)      1  2014/05/13   \n",
       "\n",
       "        GuessDay   InformDay Gender         ...          Number   LiveVill_1  \\\n",
       "0     2014/06/06  2014/06/06      男         ...               1  6400900-002   \n",
       "1250  2014/06/06  2014/06/06      男         ...               1  6400900-002   \n",
       "1251  2014/06/06  2014/06/06      男         ...               1  6400900-002   \n",
       "1252  2014/06/06  2014/06/06      男         ...               1  6400900-002   \n",
       "1253  2014/06/06  2014/06/06      男         ...               1  6400900-002   \n",
       "1     2014/06/06  2014/06/06      男         ...               1  6400900-002   \n",
       "1254  2014/06/06  2014/06/06      男         ...               1  6400900-002   \n",
       "1255  2014/06/06  2014/06/06      男         ...               1  6400900-002   \n",
       "1256  2014/06/06  2014/06/06      男         ...               1  6400900-002   \n",
       "1257  2014/06/06  2014/06/06      男         ...               1  6400900-002   \n",
       "\n",
       "       InfectVi_1  Year Month  Week         FDW startTime intTime  \\\n",
       "0     6400900-002  2014     5    20  2014/05/12         1       0   \n",
       "1250  6400900-002  2014     5    20  2014/05/12         1       0   \n",
       "1251  6400900-002  2014     5    20  2014/05/12         1       0   \n",
       "1252  6400900-002  2014     5    20  2014/05/12         1       0   \n",
       "1253  6400900-002  2014     5    20  2014/05/12         1       0   \n",
       "1     6400900-002  2014     5    20  2014/05/12         1       0   \n",
       "1254  6400900-002  2014     5    20  2014/05/12         1       0   \n",
       "1255  6400900-002  2014     5    20  2014/05/12         1       0   \n",
       "1256  6400900-002  2014     5    20  2014/05/12         1       0   \n",
       "1257  6400900-002  2014     5    20  2014/05/12         1       0   \n",
       "\n",
       "                  mstDate  \n",
       "0     2014/05/13-00:00:00  \n",
       "1250  2014/05/20-00:00:00  \n",
       "1251  2014/05/20-00:00:00  \n",
       "1252  2014/05/27-00:00:00  \n",
       "1253  2014/05/27-00:00:00  \n",
       "1     2014/05/13-00:00:00  \n",
       "1254  2014/05/20-00:00:00  \n",
       "1255  2014/05/20-00:00:00  \n",
       "1256  2014/05/27-00:00:00  \n",
       "1257  2014/05/27-00:00:00  \n",
       "\n",
       "[10 rows x 34 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points = allResults[\"points\"]\n",
    "points.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COUNTY_ID</th>\n",
       "      <th>VILLAGE_ID</th>\n",
       "      <th>VILLAGE</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>TOWN</th>\n",
       "      <th>V_ID</th>\n",
       "      <th>TOWN_ID</th>\n",
       "      <th>H_CNT</th>\n",
       "      <th>P_CNT</th>\n",
       "      <th>M_CNT</th>\n",
       "      <th>...</th>\n",
       "      <th>2014/06/24-00:00:00</th>\n",
       "      <th>2014/07/01-00:00:00</th>\n",
       "      <th>2014/07/08-00:00:00</th>\n",
       "      <th>2014/07/15-00:00:00</th>\n",
       "      <th>2014/07/22-00:00:00</th>\n",
       "      <th>2014/07/29-00:00:00</th>\n",
       "      <th>2014/08/05-00:00:00</th>\n",
       "      <th>2014/08/12-00:00:00</th>\n",
       "      <th>2014/08/19-00:00:00</th>\n",
       "      <th>2014/08/26-00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64000</td>\n",
       "      <td>066</td>\n",
       "      <td>正仁里</td>\n",
       "      <td>高雄市</td>\n",
       "      <td>苓雅區</td>\n",
       "      <td>64000080-066</td>\n",
       "      <td>64000080</td>\n",
       "      <td>1174.0</td>\n",
       "      <td>3047.0</td>\n",
       "      <td>1464.0</td>\n",
       "      <td>...</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>increase</td>\n",
       "      <td>decrease</td>\n",
       "      <td>decrease</td>\n",
       "      <td>no cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64000</td>\n",
       "      <td>067</td>\n",
       "      <td>文昌里</td>\n",
       "      <td>高雄市</td>\n",
       "      <td>苓雅區</td>\n",
       "      <td>64000080-067</td>\n",
       "      <td>64000080</td>\n",
       "      <td>1670.0</td>\n",
       "      <td>4474.0</td>\n",
       "      <td>2171.0</td>\n",
       "      <td>...</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>increase</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64000</td>\n",
       "      <td>068</td>\n",
       "      <td>建軍里</td>\n",
       "      <td>高雄市</td>\n",
       "      <td>苓雅區</td>\n",
       "      <td>64000080-068</td>\n",
       "      <td>64000080</td>\n",
       "      <td>846.0</td>\n",
       "      <td>2361.0</td>\n",
       "      <td>1209.0</td>\n",
       "      <td>...</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>increase</td>\n",
       "      <td>decrease</td>\n",
       "      <td>decrease</td>\n",
       "      <td>no cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64000</td>\n",
       "      <td>069</td>\n",
       "      <td>衛武里</td>\n",
       "      <td>高雄市</td>\n",
       "      <td>苓雅區</td>\n",
       "      <td>64000080-069</td>\n",
       "      <td>64000080</td>\n",
       "      <td>970.0</td>\n",
       "      <td>2323.0</td>\n",
       "      <td>1177.0</td>\n",
       "      <td>...</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>increase</td>\n",
       "      <td>decrease</td>\n",
       "      <td>decrease</td>\n",
       "      <td>no cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64000</td>\n",
       "      <td>001</td>\n",
       "      <td>草衙里</td>\n",
       "      <td>高雄市</td>\n",
       "      <td>前鎮區</td>\n",
       "      <td>64000090-001</td>\n",
       "      <td>64000090</td>\n",
       "      <td>4206.0</td>\n",
       "      <td>11042.0</td>\n",
       "      <td>5455.0</td>\n",
       "      <td>...</td>\n",
       "      <td>decrease</td>\n",
       "      <td>keep</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>increase</td>\n",
       "      <td>increase</td>\n",
       "      <td>decrease</td>\n",
       "      <td>increase</td>\n",
       "      <td>decrease</td>\n",
       "      <td>keep</td>\n",
       "      <td>no cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>64000</td>\n",
       "      <td>002</td>\n",
       "      <td>明孝里</td>\n",
       "      <td>高雄市</td>\n",
       "      <td>前鎮區</td>\n",
       "      <td>64000090-002</td>\n",
       "      <td>64000090</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>2807.0</td>\n",
       "      <td>1423.0</td>\n",
       "      <td>...</td>\n",
       "      <td>decrease</td>\n",
       "      <td>keep</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>64000</td>\n",
       "      <td>003</td>\n",
       "      <td>明正里</td>\n",
       "      <td>高雄市</td>\n",
       "      <td>前鎮區</td>\n",
       "      <td>64000090-003</td>\n",
       "      <td>64000090</td>\n",
       "      <td>2558.0</td>\n",
       "      <td>5959.0</td>\n",
       "      <td>3059.0</td>\n",
       "      <td>...</td>\n",
       "      <td>decrease</td>\n",
       "      <td>keep</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>increase</td>\n",
       "      <td>increase</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>increase</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>increase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>64000</td>\n",
       "      <td>004</td>\n",
       "      <td>明義里</td>\n",
       "      <td>高雄市</td>\n",
       "      <td>前鎮區</td>\n",
       "      <td>64000090-004</td>\n",
       "      <td>64000090</td>\n",
       "      <td>1117.0</td>\n",
       "      <td>2694.0</td>\n",
       "      <td>1361.0</td>\n",
       "      <td>...</td>\n",
       "      <td>decrease</td>\n",
       "      <td>keep</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>increase</td>\n",
       "      <td>increase</td>\n",
       "      <td>decrease</td>\n",
       "      <td>increase</td>\n",
       "      <td>decrease</td>\n",
       "      <td>keep</td>\n",
       "      <td>no cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>64000</td>\n",
       "      <td>005</td>\n",
       "      <td>仁愛里</td>\n",
       "      <td>高雄市</td>\n",
       "      <td>前鎮區</td>\n",
       "      <td>64000090-005</td>\n",
       "      <td>64000090</td>\n",
       "      <td>715.0</td>\n",
       "      <td>1527.0</td>\n",
       "      <td>782.0</td>\n",
       "      <td>...</td>\n",
       "      <td>decrease</td>\n",
       "      <td>keep</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>64000</td>\n",
       "      <td>006</td>\n",
       "      <td>德昌里</td>\n",
       "      <td>高雄市</td>\n",
       "      <td>前鎮區</td>\n",
       "      <td>64000090-006</td>\n",
       "      <td>64000090</td>\n",
       "      <td>975.0</td>\n",
       "      <td>2292.0</td>\n",
       "      <td>1142.0</td>\n",
       "      <td>...</td>\n",
       "      <td>decrease</td>\n",
       "      <td>keep</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "      <td>no cluster</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  COUNTY_ID VILLAGE_ID VILLAGE COUNTY TOWN          V_ID   TOWN_ID   H_CNT  \\\n",
       "0     64000        066     正仁里    高雄市  苓雅區  64000080-066  64000080  1174.0   \n",
       "1     64000        067     文昌里    高雄市  苓雅區  64000080-067  64000080  1670.0   \n",
       "2     64000        068     建軍里    高雄市  苓雅區  64000080-068  64000080   846.0   \n",
       "3     64000        069     衛武里    高雄市  苓雅區  64000080-069  64000080   970.0   \n",
       "4     64000        001     草衙里    高雄市  前鎮區  64000090-001  64000090  4206.0   \n",
       "5     64000        002     明孝里    高雄市  前鎮區  64000090-002  64000090  1150.0   \n",
       "6     64000        003     明正里    高雄市  前鎮區  64000090-003  64000090  2558.0   \n",
       "7     64000        004     明義里    高雄市  前鎮區  64000090-004  64000090  1117.0   \n",
       "8     64000        005     仁愛里    高雄市  前鎮區  64000090-005  64000090   715.0   \n",
       "9     64000        006     德昌里    高雄市  前鎮區  64000090-006  64000090   975.0   \n",
       "\n",
       "     P_CNT   M_CNT         ...          2014/06/24-00:00:00  \\\n",
       "0   3047.0  1464.0         ...                   no cluster   \n",
       "1   4474.0  2171.0         ...                   no cluster   \n",
       "2   2361.0  1209.0         ...                   no cluster   \n",
       "3   2323.0  1177.0         ...                   no cluster   \n",
       "4  11042.0  5455.0         ...                     decrease   \n",
       "5   2807.0  1423.0         ...                     decrease   \n",
       "6   5959.0  3059.0         ...                     decrease   \n",
       "7   2694.0  1361.0         ...                     decrease   \n",
       "8   1527.0   782.0         ...                     decrease   \n",
       "9   2292.0  1142.0         ...                     decrease   \n",
       "\n",
       "  2014/07/01-00:00:00  2014/07/08-00:00:00  2014/07/15-00:00:00  \\\n",
       "0          no cluster           no cluster           no cluster   \n",
       "1          no cluster           no cluster           no cluster   \n",
       "2          no cluster           no cluster           no cluster   \n",
       "3          no cluster           no cluster           no cluster   \n",
       "4                keep           no cluster             increase   \n",
       "5                keep           no cluster           no cluster   \n",
       "6                keep           no cluster             increase   \n",
       "7                keep           no cluster             increase   \n",
       "8                keep           no cluster           no cluster   \n",
       "9                keep           no cluster           no cluster   \n",
       "\n",
       "   2014/07/22-00:00:00 2014/07/29-00:00:00  2014/08/05-00:00:00  \\\n",
       "0           no cluster          no cluster             increase   \n",
       "1           no cluster          no cluster             increase   \n",
       "2           no cluster          no cluster             increase   \n",
       "3           no cluster          no cluster             increase   \n",
       "4             increase            decrease             increase   \n",
       "5           no cluster          no cluster           no cluster   \n",
       "6             increase          no cluster             increase   \n",
       "7             increase            decrease             increase   \n",
       "8           no cluster          no cluster           no cluster   \n",
       "9           no cluster          no cluster           no cluster   \n",
       "\n",
       "  2014/08/12-00:00:00 2014/08/19-00:00:00 2014/08/26-00:00:00  \n",
       "0            decrease            decrease          no cluster  \n",
       "1          no cluster          no cluster          no cluster  \n",
       "2            decrease            decrease          no cluster  \n",
       "3            decrease            decrease          no cluster  \n",
       "4            decrease                keep          no cluster  \n",
       "5          no cluster          no cluster          no cluster  \n",
       "6          no cluster          no cluster            increase  \n",
       "7            decrease                keep          no cluster  \n",
       "8          no cluster          no cluster          no cluster  \n",
       "9          no cluster          no cluster          no cluster  \n",
       "\n",
       "[10 rows x 33 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polygons = allResults[\"polygons\"]\n",
    "polygons.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Directly save the results to the perfered direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.saveAll(folder, prefix=\"mst_\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the animation of the evolutnio of polygons (.gif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.saveAnimation(figsize=(8,16), dirpath=folder, prefix=\"mst_\")"
   ]
  }
 ],
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